Why Every Engineer Should Understand Transformers
You do not need to implement a transformer from scratch to use LLMs effectively. But understanding the architecture helps you make better decisions: why context windows matter, what causes hallucination, why some tasks are hard for models, and how fine-tuning changes model behaviour.
The Core Problem: Processing Sequential Data
Before transformers, sequence models (RNNs, LSTMs) processed text token by token, left to right. This meant: information from early tokens degraded before reaching late tokens, processing was inherently sequential (slow), and long-range dependencies were hard to learn. Transformers solved all three with a single innovation: attention.
Self-Attention: How Tokens Talk to Each Other
Self-attention allows every token in the sequence to attend to every other token simultaneously. For each token, the mechanism computes: how relevant is each other token to understanding this one? The result is a weighted sum of all token representations, where the weights reflect relevance. This captures long-range dependencies without degradation and is fully parallelisable.
Multi-Head Attention
A single attention head learns one type of relationship. Multi-head attention runs multiple attention computations in parallel and concatenates the results. Each head can specialise: one head might learn syntactic relationships, another semantic ones, another coreference. GPT-4o uses hundreds of attention heads.
Positional Encoding
Since attention processes all tokens simultaneously (no left-to-right order), positional information must be injected explicitly. Early transformers used sinusoidal positional embeddings. Modern LLMs use Rotary Position Encoding (RoPE) which enables better length generalisation — the model can handle sequences longer than those seen in training.
Why Context Windows Are Expensive
Self-attention has quadratic complexity in sequence length: doubling the context quadruples the computation. A 128K token context window is 256× more expensive than a 8K context window (in attention computation). This is why efficient attention variants (Flash Attention, Sliding Window Attention) matter for long-context deployments.